Material characterization of functionally graded material by means of elastic waves and a progressive-learning neural network

In this paper, a procedure is suggested for characterizing the material pro perties of functionally graded material (FGM) plate by the use of a modifie d hybrid numerical method (HNM) and a neural network (NN). The modified HNM is used to calculate the displacement responses of FGM plate to an inciden t wave for a known material property. The NN model is trained by using the results from the modified HNM. Once trained by, the NN model can be used fo r on-line characterization of material properties if the dynamic displaceme nt responses on the surface of the FGM plate can be obtained. The material property so characterized is then used in the modified HNM to calculate the displacement responses. The NN model would go through a progressive retrai ning process until the calculated displacement responses obtained by using the characterized result is sufficiently close to the actual responses. Thi s procedure is examined for two sets of material properties of a SiC-C FGM plate. It is found that the present procedure is very robust for determinin g material property distributions in the thickness direction of FGM plates. (C) 2001 Elsevier Science Ltd. All rights reserved.